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import gradio as gr
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import spaces
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from gradio_litmodel3d import LitModel3D
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import os
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import shutil
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os.environ['SPCONV_ALGO'] = 'native'
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from typing import *
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import torch
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import numpy as np
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import imageio
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from easydict import EasyDict as edict
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from PIL import Image
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from trellis.pipelines import TrellisImageTo3DPipeline
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from trellis.representations import Gaussian, MeshExtractResult
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from trellis.utils import render_utils, postprocessing_utils
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MAX_SEED = np.iinfo(np.int32).max
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TMP_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'tmp')
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os.makedirs(TMP_DIR, exist_ok=True)
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def start_session(req: gr.Request):
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user_dir = os.path.join(TMP_DIR, str(req.session_hash))
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os.makedirs(user_dir, exist_ok=True)
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def end_session(req: gr.Request):
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user_dir = os.path.join(TMP_DIR, str(req.session_hash))
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shutil.rmtree(user_dir)
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def preprocess_image(image: Image.Image) -> Image.Image:
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"""
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Preprocess the input image.
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Args:
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image (Image.Image): The input image.
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Returns:
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Image.Image: The preprocessed image.
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"""
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processed_image = pipeline.preprocess_image(image)
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return processed_image
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def preprocess_images(images: List[Tuple[Image.Image, str]]) -> List[Image.Image]:
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"""
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Preprocess a list of input images.
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Args:
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images (List[Tuple[Image.Image, str]]): The input images.
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Returns:
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List[Image.Image]: The preprocessed images.
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"""
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images = [image[0] for image in images]
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processed_images = [pipeline.preprocess_image(image) for image in images]
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return processed_images
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def pack_state(gs: Gaussian, mesh: MeshExtractResult) -> dict:
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return {
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'gaussian': {
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**gs.init_params,
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'_xyz': gs._xyz.cpu().numpy(),
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'_features_dc': gs._features_dc.cpu().numpy(),
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'_scaling': gs._scaling.cpu().numpy(),
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'_rotation': gs._rotation.cpu().numpy(),
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'_opacity': gs._opacity.cpu().numpy(),
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},
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'mesh': {
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'vertices': mesh.vertices.cpu().numpy(),
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'faces': mesh.faces.cpu().numpy(),
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},
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}
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def unpack_state(state: dict) -> Tuple[Gaussian, edict, str]:
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gs = Gaussian(
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aabb=state['gaussian']['aabb'],
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sh_degree=state['gaussian']['sh_degree'],
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mininum_kernel_size=state['gaussian']['mininum_kernel_size'],
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scaling_bias=state['gaussian']['scaling_bias'],
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opacity_bias=state['gaussian']['opacity_bias'],
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scaling_activation=state['gaussian']['scaling_activation'],
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)
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gs._xyz = torch.tensor(state['gaussian']['_xyz'], device='cuda')
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gs._features_dc = torch.tensor(state['gaussian']['_features_dc'], device='cuda')
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gs._scaling = torch.tensor(state['gaussian']['_scaling'], device='cuda')
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gs._rotation = torch.tensor(state['gaussian']['_rotation'], device='cuda')
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gs._opacity = torch.tensor(state['gaussian']['_opacity'], device='cuda')
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mesh = edict(
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vertices=torch.tensor(state['mesh']['vertices'], device='cuda'),
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faces=torch.tensor(state['mesh']['faces'], device='cuda'),
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)
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return gs, mesh
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def get_seed(randomize_seed: bool, seed: int) -> int:
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"""
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Get the random seed.
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"""
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return np.random.randint(0, MAX_SEED) if randomize_seed else seed
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@spaces.GPU
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def image_to_3d(
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image: Image.Image,
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multiimages: List[Tuple[Image.Image, str]],
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is_multiimage: bool,
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seed: int,
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ss_guidance_strength: float,
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ss_sampling_steps: int,
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slat_guidance_strength: float,
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slat_sampling_steps: int,
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multiimage_algo: Literal["multidiffusion", "stochastic"],
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req: gr.Request,
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) -> Tuple[dict, str]:
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"""
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Convert an image to a 3D model.
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Args:
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image (Image.Image): The input image.
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multiimages (List[Tuple[Image.Image, str]]): The input images in multi-image mode.
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is_multiimage (bool): Whether is in multi-image mode.
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seed (int): The random seed.
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ss_guidance_strength (float): The guidance strength for sparse structure generation.
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ss_sampling_steps (int): The number of sampling steps for sparse structure generation.
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slat_guidance_strength (float): The guidance strength for structured latent generation.
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slat_sampling_steps (int): The number of sampling steps for structured latent generation.
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multiimage_algo (Literal["multidiffusion", "stochastic"]): The algorithm for multi-image generation.
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Returns:
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dict: The information of the generated 3D model.
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str: The path to the video of the 3D model.
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"""
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user_dir = os.path.join(TMP_DIR, str(req.session_hash))
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if not is_multiimage:
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outputs = pipeline.run(
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image,
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seed=seed,
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formats=["gaussian", "mesh"],
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preprocess_image=False,
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sparse_structure_sampler_params={
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"steps": ss_sampling_steps,
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"cfg_strength": ss_guidance_strength,
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},
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slat_sampler_params={
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"steps": slat_sampling_steps,
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"cfg_strength": slat_guidance_strength,
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},
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)
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else:
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outputs = pipeline.run_multi_image(
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[image[0] for image in multiimages],
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seed=seed,
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formats=["gaussian", "mesh"],
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preprocess_image=False,
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sparse_structure_sampler_params={
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"steps": ss_sampling_steps,
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"cfg_strength": ss_guidance_strength,
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},
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slat_sampler_params={
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"steps": slat_sampling_steps,
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"cfg_strength": slat_guidance_strength,
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},
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mode=multiimage_algo,
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)
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video = render_utils.render_video(outputs['gaussian'][0], num_frames=120)['color']
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video_geo = render_utils.render_video(outputs['mesh'][0], num_frames=120)['normal']
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video = [np.concatenate([video[i], video_geo[i]], axis=1) for i in range(len(video))]
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video_path = os.path.join(user_dir, 'sample.mp4')
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imageio.mimsave(video_path, video, fps=15)
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state = pack_state(outputs['gaussian'][0], outputs['mesh'][0])
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torch.cuda.empty_cache()
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return state, video_path
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@spaces.GPU(duration=90)
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def extract_glb(
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state: dict,
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mesh_simplify: float,
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texture_size: int,
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req: gr.Request,
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) -> Tuple[str, str]:
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"""
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Extract a GLB file from the 3D model.
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Args:
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state (dict): The state of the generated 3D model.
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mesh_simplify (float): The mesh simplification factor.
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texture_size (int): The texture resolution.
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Returns:
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str: The path to the extracted GLB file.
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"""
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user_dir = os.path.join(TMP_DIR, str(req.session_hash))
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gs, mesh = unpack_state(state)
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glb = postprocessing_utils.to_glb(gs, mesh, simplify=mesh_simplify, texture_size=texture_size, verbose=False)
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glb_path = os.path.join(user_dir, 'sample.glb')
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glb.export(glb_path)
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torch.cuda.empty_cache()
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return glb_path, glb_path
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@spaces.GPU
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def extract_gaussian(state: dict, req: gr.Request) -> Tuple[str, str]:
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"""
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Extract a Gaussian file from the 3D model.
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Args:
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state (dict): The state of the generated 3D model.
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Returns:
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str: The path to the extracted Gaussian file.
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"""
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user_dir = os.path.join(TMP_DIR, str(req.session_hash))
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gs, _ = unpack_state(state)
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gaussian_path = os.path.join(user_dir, 'sample.ply')
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gs.save_ply(gaussian_path)
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torch.cuda.empty_cache()
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return gaussian_path, gaussian_path
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def prepare_multi_example() -> List[Image.Image]:
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multi_case = list(set([i.split('_')[0] for i in os.listdir("assets/example_multi_image")]))
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images = []
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for case in multi_case:
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_images = []
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for i in range(1, 4):
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img = Image.open(f'assets/example_multi_image/{case}_{i}.png')
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W, H = img.size
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img = img.resize((int(W / H * 512), 512))
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_images.append(np.array(img))
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images.append(Image.fromarray(np.concatenate(_images, axis=1)))
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return images
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def split_image(image: Image.Image) -> List[Image.Image]:
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"""
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Split an image into multiple views.
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"""
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image = np.array(image)
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alpha = image[..., 3]
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alpha = np.any(alpha>0, axis=0)
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start_pos = np.where(~alpha[:-1] & alpha[1:])[0].tolist()
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end_pos = np.where(alpha[:-1] & ~alpha[1:])[0].tolist()
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images = []
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for s, e in zip(start_pos, end_pos):
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images.append(Image.fromarray(image[:, s:e+1]))
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return [preprocess_image(image) for image in images]
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with gr.Blocks(delete_cache=(600, 600)) as demo:
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gr.Markdown("""
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## Image to 3D Asset with [TRELLIS](https://trellis3d.github.io/)
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* Upload an image and click "Generate" to create a 3D asset. If the image has alpha channel, it be used as the mask. Otherwise, we use `rembg` to remove the background.
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* If you find the generated 3D asset satisfactory, click "Extract GLB" to extract the GLB file and download it.
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✨New: 1) Experimental multi-image support. 2) Gaussian file extraction.
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""")
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with gr.Row():
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with gr.Column():
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with gr.Tabs() as input_tabs:
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with gr.Tab(label="Single Image", id=0) as single_image_input_tab:
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image_prompt = gr.Image(label="Image Prompt", format="png", image_mode="RGBA", type="pil", height=300)
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with gr.Tab(label="Multiple Images", id=1) as multiimage_input_tab:
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multiimage_prompt = gr.Gallery(label="Image Prompt", format="png", type="pil", height=300, columns=3)
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gr.Markdown("""
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Input different views of the object in separate images.
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*NOTE: this is an experimental algorithm without training a specialized model. It may not produce the best results for all images, especially those having different poses or inconsistent details.*
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""")
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with gr.Accordion(label="Generation Settings", open=False):
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seed = gr.Slider(0, MAX_SEED, label="Seed", value=0, step=1)
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randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
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gr.Markdown("Stage 1: Sparse Structure Generation")
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with gr.Row():
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ss_guidance_strength = gr.Slider(0.0, 10.0, label="Guidance Strength", value=7.5, step=0.1)
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ss_sampling_steps = gr.Slider(1, 50, label="Sampling Steps", value=12, step=1)
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gr.Markdown("Stage 2: Structured Latent Generation")
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with gr.Row():
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slat_guidance_strength = gr.Slider(0.0, 10.0, label="Guidance Strength", value=3.0, step=0.1)
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slat_sampling_steps = gr.Slider(1, 50, label="Sampling Steps", value=12, step=1)
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multiimage_algo = gr.Radio(["stochastic", "multidiffusion"], label="Multi-image Algorithm", value="stochastic")
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generate_btn = gr.Button("Generate")
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with gr.Accordion(label="GLB Extraction Settings", open=False):
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mesh_simplify = gr.Slider(0.9, 0.98, label="Simplify", value=0.95, step=0.01)
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texture_size = gr.Slider(512, 2048, label="Texture Size", value=1024, step=512)
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with gr.Row():
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extract_glb_btn = gr.Button("Extract GLB", interactive=False)
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extract_gs_btn = gr.Button("Extract Gaussian", interactive=False)
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gr.Markdown("""
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*NOTE: Gaussian file can be very large (~50MB), it will take a while to display and download.*
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""")
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with gr.Column():
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video_output = gr.Video(label="Generated 3D Asset", autoplay=True, loop=True, height=300)
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model_output = LitModel3D(label="Extracted GLB/Gaussian", exposure=10.0, height=300)
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with gr.Row():
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download_glb = gr.DownloadButton(label="Download GLB", interactive=False)
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download_gs = gr.DownloadButton(label="Download Gaussian", interactive=False)
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is_multiimage = gr.State(False)
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output_buf = gr.State()
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with gr.Row() as single_image_example:
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examples = gr.Examples(
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examples=[
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f'assets/example_image/{image}'
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for image in os.listdir("assets/example_image")
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],
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inputs=[image_prompt],
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fn=preprocess_image,
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outputs=[image_prompt],
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run_on_click=True,
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examples_per_page=64,
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)
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with gr.Row(visible=False) as multiimage_example:
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examples_multi = gr.Examples(
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examples=prepare_multi_example(),
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inputs=[image_prompt],
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fn=split_image,
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outputs=[multiimage_prompt],
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run_on_click=True,
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examples_per_page=8,
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)
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demo.load(start_session)
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demo.unload(end_session)
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single_image_input_tab.select(
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lambda: tuple([False, gr.Row.update(visible=True), gr.Row.update(visible=False)]),
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outputs=[is_multiimage, single_image_example, multiimage_example]
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)
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multiimage_input_tab.select(
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lambda: tuple([True, gr.Row.update(visible=False), gr.Row.update(visible=True)]),
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outputs=[is_multiimage, single_image_example, multiimage_example]
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)
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image_prompt.upload(
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preprocess_image,
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inputs=[image_prompt],
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outputs=[image_prompt],
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)
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multiimage_prompt.upload(
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preprocess_images,
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inputs=[multiimage_prompt],
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outputs=[multiimage_prompt],
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)
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generate_btn.click(
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get_seed,
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inputs=[randomize_seed, seed],
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outputs=[seed],
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).then(
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image_to_3d,
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inputs=[image_prompt, multiimage_prompt, is_multiimage, seed, ss_guidance_strength, ss_sampling_steps, slat_guidance_strength, slat_sampling_steps, multiimage_algo],
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outputs=[output_buf, video_output],
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).then(
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lambda: tuple([gr.Button(interactive=True), gr.Button(interactive=True)]),
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outputs=[extract_glb_btn, extract_gs_btn],
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)
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video_output.clear(
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lambda: tuple([gr.Button(interactive=False), gr.Button(interactive=False)]),
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outputs=[extract_glb_btn, extract_gs_btn],
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)
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extract_glb_btn.click(
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extract_glb,
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inputs=[output_buf, mesh_simplify, texture_size],
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outputs=[model_output, download_glb],
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).then(
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lambda: gr.Button(interactive=True),
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outputs=[download_glb],
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)
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extract_gs_btn.click(
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extract_gaussian,
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inputs=[output_buf],
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outputs=[model_output, download_gs],
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).then(
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lambda: gr.Button(interactive=True),
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outputs=[download_gs],
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)
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model_output.clear(
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lambda: gr.Button(interactive=False),
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outputs=[download_glb],
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)
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if __name__ == "__main__":
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pipeline = TrellisImageTo3DPipeline.from_pretrained("JeffreyXiang/TRELLIS-image-large")
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pipeline.cuda()
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try:
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pipeline.preprocess_image(Image.fromarray(np.zeros((512, 512, 3), dtype=np.uint8)))
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except:
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pass
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demo.launch()
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